A Study Of Hybrid Genetic Algorithm

Genetic Algorithm(for short, GA or GAs), which has a simple, all-purpose, sure character, has made great achievements in the solution to difficult, complex problems, and can convergence to the global minimum. However, random search techniques handicap convergence steep of Genetic Algorithm which likely to seek out a local optimum solution but a global sub-optimization. Traditional methods, which utilize the problem's information and correlative knowledge, seek to next solution from an initialize point in search space with certain definitional criterion, with purposeful search, steep convergence and easily solve to a local minimum. The hybrid Genetic Algorithm(for short, HGA) which incorporate the GAs into the tradition methods can surely be studied well. GA is studied in the thesis which introduce the circumstance, character, methods, convergence and application of HGA which is signed out for the purpose of prevention of premature phenomenon, acceleration of convergence and improvement of all-purpose character. Numerical experiments of C and Matlab language show these HGA work well in chapter 4.